AIMC Topic: Coronary Disease
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Predicting Coronary Heart Disease Using Data Mining and Machine Learning Solutions.
Anais da Academia Brasileira de Ciencias
Jun 16, 2025
This research focuses on predicting cardiovascular disease using machine learning classification strategies. The study presents a unique approach by integrating multiple machine learning techniques, leveraging the strengths of Random Forest and Gradi...
Predicting coronary heart disease with advanced machine learning classifiers for improved cardiovascular risk assessment.
Scientific reports
Apr 17, 2025
Worldwide, coronary heart disease (CHD) is a leading cause of mortality, and its early prediction remains a critical challenge in clinical data analysis. Machine learning (ML) offers valuable diagnostic support by leveraging healthcare data to enhanc...
X-ray Coronary Angiogram images and SYNTAX score to develop Machine-Learning algorithms for CHD Diagnosis.
Scientific data
Mar 21, 2025
Coronary Heart Disease (CHD) is becoming a leading cause of death worldwide. To assess coronary artery narrowing or stenosis, doctors use coronary angiography, which is considered the gold-standard method. Interventional cardiologists rely on angiogr...
An efficient artificial neural network-based optimization techniques for the early prediction of coronary heart disease: comprehensive analysis.
Scientific reports
Feb 9, 2025
Coronary heart disease (CHD) is the world's leading cause of death, contributing to a high mortality rate. This emphasizes the requirement for an advanced decision support system in order to evaluate the risk of CHD. This study presents an Artificial...
Explainable machine learning model for assessing health status in patients with comorbid coronary heart disease and depression: Development and validation study.
International journal of medical informatics
Jan 23, 2025
BACKGROUND: Coronary heart disease (CHD) and depression frequently co-occur, significantly impacting patient outcomes. However, comprehensive health status assessment tools for this complex population are lacking. This study aimed to develop and vali...
Coronary health index based on immunoglobulin light chains to assess coronary heart disease risk with machine learning: a diagnostic trial.
Journal of translational medicine
Jan 6, 2025
BACKGROUND: Recent studies suggest a connection between immunoglobulin light chains (IgLCs) and coronary heart disease (CHD). However, current diagnostic methods using peripheral blood IgLCs levels or subtype ratios show limited accuracy for CHD, lac...
A machine learning algorithm for the identification elevated Lp(a) in patients with, or high-risk of having, coronary heart disease.
International journal of cardiology
Oct 2, 2024
BACKGROUND: Decision tree algorithms, obtained by machine learning, provide clusters of patients with similar clinical patterns by the identification of variables that best merge with a given dependent variable.
Exploration of fractional flow reservation score based on artificial intelligence post-processing for coronary artery lesions in patients with diabetes and coronary heart disease.
SLAS technology
Sep 21, 2024
In order to evaluate the relationship between coronary heart disease (CHD) and fractional flow reservation (FFR) in patients with different levels of CHD and diabetes, this paper used AI (artificial intelligence) post-processing technology to detect ...
Predictive model and risk analysis for coronary heart disease in people living with HIV using machine learning.
BMC medical informatics and decision making
Apr 25, 2024
OBJECTIVE: This study aimed to construct a coronary heart disease (CHD) risk-prediction model in people living with human immunodeficiency virus (PLHIV) with the help of machine learning (ML) per electronic medical records (EMRs).
Diagnostic accuracy of artificial intelligence-enabled vectorcardiography versus myocardial perfusion SPECT in patients with suspected or known coronary heart disease.
Nuklearmedizin. Nuclear medicine
Feb 20, 2024
AIM: The present study evaluated with myocardial perfusion SPECT (MPS) the diagnostic accuracy of an artificial intelligence-enabled vectorcardiography system (Cardisiography, CSG) for detection of perfusion abnormalities.
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